Sleep arousals are typically defined as short intrusions of wakefulness into sleep that are presently quantified by manual sleep staging of electroencephalograms (EEGs) by trained clinicians. Due to mounting evidence that relates arousals to sleep disorders, there has been a growing interest in automating this approach. In this study, we aim to detect non-apnea events, respiratory-effort-related-arousals (RERA), and non-RERA arousals from a range of physiological signals recorded during a single night of sleep for the Physionet/CinC 2018.
The proposed approach made use of the following signals: oxygen saturation (SaO2), electrocardiogram (ECG), averaged central EEG, electrooculogram (EOG) and electromyogram. After a simple bandpass/notch preprocessing using FIR filters, each raw input was segmented into 10s epochs with 1s shifts. 145 features were calculated for each epoch such as heart rate variability metrics (ECG) and spectral band powers (EEG/EOG). Dimension reduction was then performed using the first 20 components of Principal Component Analysis (PCA), and a Hidden Markov Model (HMM) trained on each subject. A 10-fold cross-validation was used to assess the method's performance using sensitivity (SE), positive predictive value (PPV), area under curve (AUC) and F1-score (F1). For each fold, the trained HMM models were averaged and applied to the validation set. Segments with undefined labels were excluded from training.
While sensitivity was high, F1 remained at 11.2%. This suggests that the model has a propensity to classify epochs as arousals and reveals the need for a more informative set of features. A strong overlap in the features between arousals and non-arousals exists (even after PCA) as well as class imbalance with arousal periods occurring during <7% of time. Future work will focus on extending the current HMM approach and using deep learning techniques to identify such features in a more data-driven fashion, by better utilising the high dimensionality of the dataset.